One method of analyzing the state of a muscle is to collect measurements of electrical signals associated with the activity of that muscle. This type of measurement is known as electromyographic (EMG) measurement, and it may be performed using either invasive (percutaneous) or non-invasive techniques. EMG measurements have been used in a number of different medical applications including the treatment and possible diagnosis of lower back pain.
While percutaneous EMG techniques have been accepted in medicine as accurate for measuring the electrical activity of an underlying muscle, their use is often undesirable or unacceptable. That is, percutaneous EMG techniques require additional materials and expertise, and they present risks not found with non-invasive techniques.
Alternatively, evaluating muscle activity using non-invasive or surface EMG (sEMG) measurements has attracted interest from scientists and medical practitioners for the last 30 years with its promise as an objective, painless muscle measurement technique.
Measurements of surface electrical activity, or any other clinical measurement, must meet several objectives and criteria relating to reliability in order to be considered useful for providing diagnostic or evaluative information. For example, the electrical activity signal measured should be objectively defined and reproducible. The information obtained should meet a need that is best met by making surface EMG measurements. Further, the information should be usable and easily interpreted by the level of skill of practitioners for which it is intended. Finally, the process should be cost-effective and have universal application as either an assessment or therapeutic system or both.
To meet these objectives, the evaluation system should reliably differentiate between healthy, normal, pain-free subjects and subjects with muscle disorders. The evaluation system should also report results with an extremely high level of statistical certainty.
Of the many possible applications for surface EMG measurement, back function evaluation is one of the most suitable. A relatively large percentage of the population experiences back pain that could be attributed to soft tissue damage, i.e., muscle dysfunction. Traditional evaluation techniques have not been effective at objectively determining muscle dysfunction responsible for such pain.
Typical clinical evaluation techniques have relied upon subjective evaluations by the patient to determine the nature of the dysfunction. That is, the patient is usually asked to perform certain motions, and depending upon the patient's ability to perform these motions within subjective pain parameters, a diagnosis is made.
Further, from an economic standpoint, a large percentage of insurance claims are made by individuals claiming to have muscle back pain. Because of the subjective nature of the testing, these claims usually cannot be objectively verified. Accordingly, there is a large potential for fraudulent claims being filed at a substantial cost to insurance companies and ultimately, the consuming public.
A muscle assessment system should be a capable of making significant comparisons of any given patient to a normative group. Because of the comparative nature of the assessment process, the importance of having an evaluation system capable of producing reproducible data becomes paramount.
In the past, studies that have attempted to achieve reproducibility, or to minimize the variation in data, used the maximum voluntary contraction (MVC) method of normalization. This technique requires high levels of muscle activation, causing the engagement of fast-twitch motor units not ordinarily activated in normal movements. That is, these studies compare the measured muscle activity during evaluation to an MVC.
In normal muscle, the slow-twitch motor units produce most of their fused tension before fast-twitch motor units begin to add to muscle force. The addition of fast-twitch motor units in MVC causes a disproportionate increase in the sEMG. The inclusion of fast-twitch motor units, which are seldom used in everyday functioning, occurs with the MVC condition and influences the anatomical distribution and force-voltage relationship of EMG data. Moreover, MVC runs the risk of exacerbating pain and doing further damage to dysfunctional muscles.
Clinical use of sEMG has failed to produce a sufficiently objective evaluation of muscle health. In much of the literature relating to back muscle evaluation, equivalence is sought between EMG resting levels and painful muscles or back pain in general. However, static resting measurements are greatly influenced by small postural adjustment that cannot be adequately controlled. Accordingly, the postural and instrumental error can become so large so as to obscure useful information.
Accordingly, a need exists for a system and method that correctly characterizes muscle dysfunction with a high degree of reproducibility. Further, such a system and method should allow for normalization of data using normally activated muscle values.
U.S. Pat. No. 5,502,208, entitled "Method for Determining Muscle Dysfunction," issued to Toomin et al. ("the '208 patent") and incorporated herein by reference, discloses a method and system that seeks to achieve the above objectives. However, the method and system disclosed in the '208 patent features several disadvantages.
The method disclosed in the '208 patent employs a process that discretely quantifies all of the data elements under analysis. One disadvantage of this method is that it cannot account for data that falls short of subjectively predetermined cutoff points, regardless of the proximity to those cutoff points or the consistency of the data that falls beneath a cutoff point.
For example, the method disclosed in the '208 patent would be indifferent to the following hypothetical case: Out of 100 data elements total, if 50 of them have achieved between 80% and 90% of the predetermined cutoff point, but only ten of them exceeded the cutoff point, then the former 50 data elements would simply be labeled "normal," and discarded from further analysis. Those 50 data elements would not contribute to the final result, despite their close proximity to the cutoff point and their significant number, i.e., their consistency. The system would then use only ten out of 100 elements to make its determination.
The method and system disclosed in the '208 patent is also vulnerable to measurement error. By using discrete quantification, this method allows for opportunities for measured data to fall on one side of the cutoff points on one occasion, and to fall on the other side of the cutoff points if measured on a different occasion, potentially yielding very different results for each occasion.
Moreover, the method and apparatus disclosed in the '208 patent presents its ultimate finding in a broad classification system, wherein each muscle under diagnosis is assigned one of a handful of categories in this classification system: "normal," "symptomatic," "dysfunctional," etc. These terms are only defined as being relative to one another. For example, "symptomatic" is considered more severe than "normal," and "dysfunctional" is more severe than "symptomatic." However, this classification system presents no true or absolute indication of the degree of departure from an ideal or absolute normal condition of the muscle under evaluation.
Another disadvantage of the apparatus disclosed in the '208 patent is that it uses adipose corrections provided from a table, the contents of which depend upon an adipose tissue measuring device that has since been found to be somewhat unreliable.
Another further disadvantage of the method and apparatus disclosed in the '208 patent is that it relies heavily on an assumed normal or Gaussian distribution of data within the normative database. This method is therefore susceptible to error arising from departures from a normal distribution in the actual data collected and analyzed.